65 research outputs found

    A longitudinal examination of the influence of maturation on physical self-perceptions and the relationship with physical activity in early adolescent girls

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    This longitudinal study investigated the influence of maturation on physical self-perceptions and the relationship with physical activity in early adolescent girls (N = 150; mean age = 12.79 0.31). Physical characteristics were measured and participants completed the Physical Activity Questionnaire for Children, the Children and Youth Physical Self-Perception Profile and the Pubertal Development Scale on two occasions 12 months apart. The results demonstrated a decrease in overall physical activity levels over 12 months which was not influenced by maturational status or physical characteristics. Additional analysis indicated that physical self-perceptions partially accounted for the explained variance in physical activity change, with physical condition being an important individual predictor of physical activity. Further analysis indicated that body mass was an important individual predictor of changes in perceptions of body attractiveness and physical self-worth. At this age maturation has a limited influence on the physical activity behaviours of early adolescent girls and although the variance in physical activity was partly accounted for by physical self-perceptions, this was a relatively small contribution and other factors related to this drop in physical activity need to be considered longitudinally

    How much walking should be advocated for good health in adolescent girls?

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    Background:\ud It is currently not known how much walking should be advocated for good health in adolescent girls. The aim of this study was therefore to recommend health referenced standards for step defined physical activity relating to appropriate health criterion/indicators in a group of adolescent girls.\ud \ud Method:\ud Two hundred and thirty adolescent girls aged between 12-15years volunteered to take part in the study. Each participant undertook measurements (BMI, waist circumference, % body fat and blood pressure) to define health status. Activity data were collected by pedometer and used to assess daily step counts and accumulated daily activity time over seven consecutive days.\ud \ud Results:\ud Individuals classified as ‘healthy’ did not take significantly more steps·day−1 nor spend more time in moderate intensity activity than individuals classified as at health risk or with poor health profiles.\ud \ud Conclusion:\ud ‘Healthy’ adolescent girls do not walk significantly more in term of steps·day−1 or time spent in activity than girls classified as ‘unhealthy’. This could suggest that adolescent girls may not walk enough to stratify health and health related outcomes and as a result the data could not be used to inform an appropriate step guideline for this population

    Maturational differences in physical self-perceptions and the relationship with physical activity in early adolescent girls

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    This cross-sectional study examined the effect of physical self-perceptions and maturation on physical activity, and considered the influence of maturation and age on physical self-perceptions in early adolescent girls (n = 208; mean age = 11.83 ± 0.39 years). Participants completed the Physical Activity Questionnaire for Older Children, the Children’s Physical Self-Perception Profile and the Pubertal Development Scale. Results indicated that the girls were relatively active and physical self-perceptions were significantly and moderately correlated with physical activity. There were no differences in physical activity between maturation stages. There was evidence of an inverse relationship between aspects of physical self-perceptions and maturation, but not with chronological age. This study has identified preliminary evidence for an interaction between maturation, physical self-perceptions and physical activity, but longitudinal research is required to examine this in more detail


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    The purpose of this study was to introduce an automatic method for calculating personal body segment parameters (BSPs). In this automatic method, a Microsoft Kinect device was used to capture depth frames for measuring joint locations. The open source software, MakeHuman, was used for generating 3D human models by referring using the joint location data captured from the depth frames. Segmental meshes were obtained from the generated 3D human models and personal BSPs could be calculated automatically. The tests showed that the developed method can complete all of the processes without manual digitizing, anatomical landmark detection and medical scanner operation. Further research should be conducted to establish the accuracy of the segmental masses, centres of mass and moments of inertia acquired from the developed methods

    No Acute Effect of Reduced-exertion High-intensity Interval Training (REHIT) on Insulin Sensitivity

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    We have previously demonstrated that reduced-exertion high-intensity interval training (REHIT), requiring a maximum of two 20-s all-out cycling sprints in a 10-min exercise session, improves insulin sensitivity in sedentary men over a 6-week training intervention. However, the acute effects of REHIT on insulin sensitivity have not previously been described. In this study 14 men and women (mean±SD age: 23±5 years; BMI 22.7±4.7 kg·m−2; +˙VO2max: 37.4±8.6 mL·kg−1·min−1) underwent oral glucose tolerance testing 14–16 h after an acute bout of reduced-exertion high-intensity interval training (2×20-s all-out sprints; REHIT), moderate-vigorous aerobic exercise (45 min at ~75% VO2max; AER), and a resting control condition (REST). Neither REHIT nor AER was associated with significant changes in glucose AUC (REHIT 609±98 vs. AER 651±85 vs. REST 641±126 mmol·l−1·120 min), insulin AUC (REHIT 30.9±15.4 vs. AER 31.4±13.0 vs. REST 35.0±18.5 nmol·l−1·120 min) or insulin sensitivity estimated by the Cederholm index (REHIT 86±20 vs. AER 79±13 vs. REST 82±24 mg·l2·mmol−1·mU−1·min−1). These data suggest that improvements in insulin sensitivity following a chronic REHIT intervention are the result of training adaptations rather than acute effects of the last exercise session

    Automated body volume acquisitions from 3D structured-light scanning

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    Whole-body volumes and segmental volumes are highly related to the health and medical condition of individuals. However, the traditional manual post-processing of raw 3D scanned data is time-consuming and needs technical expertise. The purpose of this study was to develop bespoke software for obtaining whole-body volumes and segmental volumes from raw 3D scanned data automatically and to establish its accuracy and reliability. The bespoke software applied Stitched Puppet model fitting techniques to deform template models to fit the 3D raw scanned data to identify the segmental endpoints and determine their locations. Finally, the bespoke software used the location information of segmental endpoints to set segmental boundaries on the reconstructed meshes and to calculate body volume. The whole-body volumes and segmental volumes (head & neck, torso, arms, and legs) of 29 participants processed by the traditional manual operation were regarded as the references and compared to the measurements obtained with the bespoke software using the intra-method and inter-method relative technical errors of measurement. The results showed that the errors in whole-body volumes and most segmental volumes acquired from the bespoke software were less than 5%. Overall, the bespoke software developed in this study can complete the post-processing tasks without any technical expertise, and the obtained whole-body volumes and segmental volumes can achieve good accuracy for some applications in health and medicine